%0 Journal Article %1 Liu2017Selection %A Liu, Xiao %A Wang, Bao-Jin J. %A Xu, Luo %A Tang, Hong-Ling L. %A Xu, Guo-Qing Q. %D 2017 %J PloS one %K gene-essentiality machine-learning %N 3 %T Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species. %U http://view.ncbi.nlm.nih.gov/pubmed/28358836 %V 12 %X Genes that are indispensable for survival are essential genes. Many features have been proposed for computational prediction of essential genes. In this paper, the least absolute shrinkage and selection operator method was used to screen key sequence-based features related to gene essentiality. To assess the effects, the selected features were used to predict the essential genes from 31 bacterial species based on a support vector machine classifier. For all 31 bacterial objects (21 Gram-negative objects and ten Gram-positive objects), the features in the three datasets were reduced from 57, 59, and 58, to 40, 37, and 38, respectively, without loss of prediction accuracy. Results showed that some features were redundant for gene essentiality, so could be eliminated from future analyses. The selected features contained more complex (or key) biological information for gene essentiality, and could be of use in related research projects, such as gene prediction, synthetic biology, and drug design.

@article{Liu2017Selection, abstract = {Genes that are indispensable for survival are essential genes. Many features have been proposed for computational prediction of essential genes. In this paper, the least absolute shrinkage and selection operator method was used to screen key sequence-based features related to gene essentiality. To assess the effects, the selected features were used to predict the essential genes from 31 bacterial species based on a support vector machine classifier. For all 31 bacterial objects (21 Gram-negative objects and ten Gram-positive objects), the features in the three datasets were reduced from 57, 59, and 58, to 40, 37, and 38, respectively, without loss of prediction accuracy. Results showed that some features were redundant for gene essentiality, so could be eliminated from future analyses. The selected features contained more complex (or key) biological information for gene essentiality, and could be of use in related research projects, such as gene prediction, synthetic biology, and drug design.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Liu, Xiao and Wang, Bao-Jin J. and Xu, Luo and Tang, Hong-Ling L. and Xu, Guo-Qing Q.}, biburl = {https://www.bibsonomy.org/bibtex/28475bcce6614dfb21291a4338b99d737/karthikraman}, citeulike-article-id = {14381023}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/28358836}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=28358836}, interhash = {d880d69c042b8b6fc35c9c2a9ce807f6}, intrahash = {8475bcce6614dfb21291a4338b99d737}, issn = {1932-6203}, journal = {PloS one}, keywords = {gene-essentiality machine-learning}, number = 3, pmid = {28358836}, posted-at = {2017-06-22 15:08:25}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Selection of key sequence-based features for prediction of essential genes in 31 diverse bacterial species.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/28358836}, volume = 12, year = 2017 }